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Using Ai to Automatically Diagnose Alzheimer’s Disease

Researchers from Stanford University have developed a deep learning based system that can automatically detect Alzheimer’s disease and its biomarkers from MRIs, with 94 percent accuracy. “Our method uses minimal preprocessing of MRIs (imposing minimum preprocessing artifacts) and utilizes a simple data augmentation strategy of downsampled MR images for training purposes,” the researchers stated in their paper.

New Paper: Survey of FPGAs for Convolutional Neural Networks

Sparsh Mittal has just published a new paper on the use of FPGAs for Convolutional Neural Networks. “Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of cognitive tasks and due to this, they have received significant interest from tnewhe researchers. Given the high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy-efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of CNNs.”

Survey Foretells Explosive Growth in Machine Learning Projects Over Next Two Years

Over at the Univa Blog, Gary Tyreman writes that the company sponsored an industry-wide survey to better understand what key challenges our HPC users are currently facing that are preventing them from moving their machine learning (ML) projects into production. “Our goal is to use this data to help guide our customers and recommend the right set of tools and migration options needed to accelerate value in machine learning.”

A Guide to High Performance Computing in the AWS Cloud

A new ebook from Intel and AWS explores how AWS cloud can mitigate some of today’s top HPC challenges and enable engineers and researchers to innovate without constraints. “Most HPC processing is still conducted using on-premises systems. But for those companies unwilling to invest in new hardware to handle peak demands, moving HPC workloads to the cloud may be the answer.”

HPC Cloud Drives Innovation & Accelerates Time to Results

HPC applications and workloads have been constrained by limited on-premises infrastructure capacity, high capital expenditures and the constant need for technology refreshes. But there may be an answer: running your HPC workloads in the cloud. “According to the report, hundreds of companies in life sciences, financial services, manufacturing, energy and geo sciences, media and entertainment are solving complex challenges and speeding up time to results using HPC on AWS.”

UberCloud Works with ANSYS and Azure to Optimize Bioreactors

Over at The UberCloud, Wolfgang Gentzsch writes that researchers are using HPC in the Cloud in a revolutionary new way for the design of bioreactors. “It’s amazing to think of all the products created in bioreactors. The medications we take, the beer we drink and the yogurt we eat are all made in bioreactors optimized for the manufacturing of these products. Unfortunately, optimizations take a lot of work and data. In a new HPC Cloud project, engineers from ANSYS with support from UberCloud performed an extensive Design of Experiments in Microsoft’s Azure Cloud.”

Virtualization Adds Value, Benefits to HPC Environments

VMware explores the concepts of virtual throughput clusters and CPU overcommitment with VMware vSphere to create multitenant and agile virtual HPC computing environments.

Ubercloud Posts 2018 Compendium of HPC Cloud Case Studies

Our friends at the UberCloud have published their 2018 Compendium of HPC Cloud Case Studies. “The Annual UberCloud Compendium of Case Studies is out! This is the 5th edition of this set of case studies relating to the world of Technical Computing. This year’s collection features case studies based on 13 select UberCloud Experiments, and the whole project is sponsored once again by Hewlett Packard Enterprise and Intel.”

Where Ready Nodes are the Optimal Choice

The perennial question for IT asset acquisition is whether to build or consume, and there is no ‘one size fits all’ answer to the question. Individual components provide the maximum flexibility, but also require the maximum of self support. Reference architectures provide the recipe and guidance, but the customer still has the responsibility for making the system work.

Network Switch Configuration with Dell EMC Ready Nodes

Storage networks are constantly evolving. From traditional Fibre Channel to IP-based storage networks, each technology has its place in the data center. IP-based storage solutions have two main network topologies to choose from based on the technology and administration requirements. Dedicated storage network topology, shared leaf-spine network, software defined storage, and iSCSI SAN are all supported. Hybrid network architectures are common, but add to the complexity.